Method for Joint Antenna-Array Calibration and Direction of Arrival Estimation for Automotive Applications

ABSTRACT

This invention is present an iterative method for joint antenna array calibration and direction of arrival estimation using millimeter-wave (mm-Wave) radar. The calibration compensates for array coupling, phase, and gain errors and does not require any training data. This method is well suited for applications where multiple antenna elements are packaged in a chip and where offline calibration is either expensive or is not possible. This invention is also effective when the array coupling is a function of direction of arriving waves from the object. It is also applicable to any two-dimensional array shape. Real experiment results demonstrate the viability of the algorithm using real data collected from a four-element array.

CLAIM OF PRIORITY

This application claims priority under 35 U.S.C 119(e)(1) to U.S. Provisional Application No. 62/233,055 filed Sep. 25, 2015

TECHNICAL FIELD OF THE INVENTION

The technical field of this invention is antenna array calibration.

BACKGROUND OF THE INVENTION

This invention estimates the coupling between radar antenna array elements as well as phase and gain of each element. At the same time, the invention estimates the direction of arrival of objects in radar's field of view. The invention does not require any use of training data for antenna array calibration, and it is applicable to any array shape.

SUMMARY OF THE INVENTION

An iterative method is shown for joint antenna array calibration and direction of arrival estimation on a millimeter wave radar. The calibration compensates for array coupling, phase and gain errors without the requirement for training data. The method shown is also effective when the array coupling is a function of the direction of the arriving waves, and is applicable to two dimensional array shapes.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of this invention are illustrated in the drawings, in which:

FIG. 1 shows a block diagram of the invention;

FIG. 2 illustrates an exemplary L-element antenna array;

FIG. 3 is a block diagram of one example implementation; and

FIG. 4 illustrates the estimated and true elevation and azimuth angles in degrees versus azimuth.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Recent years have witnessed widespread use of millimeter wave (mm-Wave) radars for advanced driver assistance system (ADAS) applications. Compared with other sensing modalities such as a camera, radar has the ability to perform equally well during different times of the day and can be deployed out of sight behind the car bumper or the doors. In many ADAS applications such as parking, cruise control, and braking, the radar is primarily used to find the three-dimensional location of objects around the vehicle. This includes range, azimuth angle, and elevation angle. The range is computed from the round trip delay of the transmitted signal and the two-dimensional (2D) angle is estimated by using the data collected by an antenna array employing a beamforming-based or an eigen-decomposition based high-resolution frequency estimation method. It is well known that these directions of arrival (DOA) estimates are highly biased if the coupling between antenna array elements is not corrected and compensated for. Additionally, the phase and gain mismatch among antenna elements also adversely affects the estimation. The impact of these non-idealities is pronounced when the antennas are placed very close to each other, which generally is the case in automotive radars.

Calibration for antenna coupling has been widely studied in the past and many methods have been proposed. Generally, training data is collected by L-element antenna array from radar field of view and a L×L calibration matrix is estimated; this matrix is then applied to signal received by the array. While this methodology works well for many cases, it is not very well suited for automotive radar applications for the following reasons. First, the antenna coupling for a 2D array changes with DOA from objects. It is, therefore, not possible to estimate a calibration matrix that would be applicable for all directions in radar field of view. Secondly, in automotive radars where multiple radars are placed on the car and are thus produced in high volume, it is desired that the calibration is done online without any need for training data.

This invention is a joint array calibration and 2D angle estimation method of multiple objects around the vehicle. The method does not require any training data and needs minimal supervision. Whereas in the past, the joint estimation problem was solved for specific array shapes, the problem formulation and optimization proposed in this application can be applied to any array design and shape. We will present experimental results using data collected from a 77 GHz radar with a four-element antenna array to show efficacy of the proposed method. A simplified block diagram of such an implementation is shown in FIG. 3, where 301 is a processor, 302 is the radio frequency front end, and 303 is the antenna array.

Though the analysis below applies to any 2D array shape. we consider a rectangular, L-element antenna array shown in FIG. 2, where L−1 elements 201 through 202 are in one direction, and the L^(th) element 203 is in the orthogonal direction. The array response at time n is given by

x(n)=BΓAs(n),  (1)

where x(n)=[x1(n), x2(n), . . . , xL(n)]^(T), B is a L×L coupling matrix, Γ={α₁e^(−jωψ) ¹ , α₁e^(−jωψ) ² , . . . , α₁e^(−jωψ) ^(L) , } is L×L matrix with antenna gains and phase values as diagonal elements. A=[a(θ₁, φ₁), a(θ₂, φ₂), . . . , a(θ_(K), φ_(K))] is a L×K matrix of steering vectors with a(θ_(l), φ_(l)) given by

$\begin{bmatrix} {1,^{{- j}\frac{2\pi}{\lambda}d_{x}{\sin {(\theta_{l})}}{\cos {(\varphi_{l})}}},^{{- j}\frac{2{({L - 1})}\pi}{\lambda}d_{x}{\sin {(\theta_{l})}}{\cos {(\varphi_{l})}}},\ldots} \\ ^{- {j{({{\frac{2\pi}{\lambda}d_{x}{\sin {(\theta_{l})}}{\cos {(\varphi_{l})}}} + {\frac{2\pi}{\lambda}d_{y}{\sin {(\varphi_{l})}}}})}}} \end{bmatrix}^{T},$

s(n)=[s₁(n), s₂(n), . . . , s_(K)(n)]^(T) is complex signal amplitude, K is the number of objects, θ is azimuth direction of arrival, φ is elevation direction of arrival, and (•)^(T) denotes conjugate transpose. The parameters α_(l) and ψ_(l) are gain and delay associated with lth sensor. Biased DOA estimates are obtained if the effects of B and Γ are not compensated for in the received signal.

Let λ_(i) and u_(i), i=1, 2, . . . , L be the eigenvalues and eigenvectors of the sample covariance matrix R_(x)=E{xx^(H)}. Collecting the set of eigenvectors belonging to noise subspace in matrix U=[u_(K+1), u_(K+2), . . . , u_(L)], the unknowns Γ, B, θ, and φ are obtained by minimizing the cost function:

$\begin{matrix} {{J = {\sum\limits_{k = 1}^{K}\; {{U^{H}B\; \Gamma \; {a\left( {\theta_{k},\varphi_{k}} \right)}}}^{2}}},} & (2) \end{matrix}$

where denotes Frobenius norm.

In the proposed method, the matrices Γ and B, and the angles (θ, φ) are estimated using the following iterative method as shown in FIG. 1:

1) Initialization (101); i=0; Set B^(i) and Γ^(i) to initial values.

2) Estimate data covariance matrix (102)

$\begin{matrix} {{\hat{R}}_{x} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\; {{x(n)}{x(n)}^{H}}}}} & (3) \end{matrix}$

3) Eigen-decompose {circumflex over (R)}_(x) (103), find U and search for K peaks (104) in the 2D spectrum defined by

P ^(i)(θ, φ)=∥U ^(H) B ^(i)Γ^(i) a(θ, φ)∥⁻²  (4)

The peaks of equation (4) correspond to the DOA estimates {(θ_(k), φ_(k))}_(k=1) ^(K).

4) Use the estimated DOA to form the matrix A. (105)

5) Under the constraint γ^(H)w=1, where w=[1, 0, 0, . . . , 0]^(T), estimate L×1 vector γ (106)

γ=Z ⁻¹ w/(w ^(T) Z ⁻¹ w),  (5)

where the matrix Z is given by =

$\begin{matrix} {Z = {\sum\limits_{k = 1}^{K}\; {Q_{k}^{H}B^{i^{H}}{UU}^{H}B^{i}Q_{k}}}} & (6) \end{matrix}$

and the diagonal matrix Qk is formed using Qk=diag{a(θ_(k), φ_(k))}.

6) Update the estimate of Γ (107) using the diagonal elements of γ as Γ^(i) ⁺¹ =diag{γ}.

7) Finally, the cost function in equation (2) is minimized (108) in the least squares sense to solve for B under the constraint B₁₁=1. This optimization is carried out as follows:

Compute KL×L² matrix M=(A^(T)B^(iT))

U^(T), where

defines the Kronecker product.

Extract M₁=M(:, 1) and M₂=M(:, 2:end); i.e, M₁ contains only the first column of M and M₂ is M, except for its first column.

Compute (L²−1)×1 vector b=−M₂ ^(#)M₁, where (•)^(#) denotes pseudo-inverse.

Compute L²×1 vector {tilde over (b)}=[1 b^(T)]^(T).

Re-arrange {tilde over (b)} in rows of L to form updated B^(i) ⁺¹ .

The iterative calibration and angle estimation is continued until the cost function at (i+1)th iteration is smaller than what it was at the ith iteration by a pre-set threshold.

FIG. 4 illustrates the case using a four-element array (L=4) with three elements in one direction and the fourth element in orthogonal direction with a carrier frequency of 77 GHz. The inter-element spacing in either direction was 2 mm. An object was placed at an elevation angle of −20 degrees and moved along azimuth direction from −40 degrees to 40 degrees in increments of 5 degrees. The matrices B and Γ were initialized with identity matrices. At each location of the object, the joint iterative algorithm was used to estimate the DOA's. It took a maximum of 5 iterations for the algorithm to converge to an estimate. FIG. 4 illustrates the azimuth and elevation angles plotted against the azimuth angles.

A joint calibration and angle estimation algorithm is presented. The method is especially suited for automotive applications where multiple sensors are installed around the vehicle and online calibration and angle estimation is highly desired. The method does not impose any constraint on the array shape and structure and takes only a few iterations to converge. 

What is claimed is:
 1. A method of joint antenna array calibration and direction of arrival estimation comprising the iterative steps of: estimating the data covariance matrix {circumflex over (R)}_(x); Eigen decomposing {circumflex over (R)}_(x); finding U and K peaks in the 2D spectrum defined by P ^(i)(θ, φ)=∥U ^(H) B ^(i)Γ^(i) a(θ, φ)∥⁻² where the peaks correspond to the direction of arrival (DOA) estimates; forming matrix A using the estimated DOA values; calculating matrix Z; estimating vector γ; forming diagonal matrix Qk; updating the estimated Γ; and minimizing the cost function.
 2. The method of claim 1, wherein: said covariance matrix is estimated by ${\hat{R}}_{x} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\; {{x(n)}{{x(n)}^{H}.}}}}$
 3. The method of claim 1 wherein: said matrix Z is calculated by $Z = {\sum\limits_{k = 1}^{K}\; {Q_{k}^{H}B^{i^{H}}{UU}^{H}B^{i}{Q_{k}.}}}$
 4. The method of claim 1, wherein: said vector γ is estimated where γ=Z⁻¹w/(w^(T)Z⁻¹w).
 5. The method of claim 1 wherein: Said diagonal matrix QK is formed using Qk=diag{a(θk, θk)}.
 6. The method of claim 1 wherein: The estimated Γ is updated using the diagonal elements of γ as Γ^(i) ⁺¹ =diag{γ}.
 7. The method of claim 1, further comprising the steps of: Calculating a cost factor by $J = {\sum\limits_{k = 1}^{K}\; {{U^{H}B\; \Gamma \; {a\left( {\theta_{k},\varphi_{k}} \right)}}}^{2}}$ Minimizing the cost factor by Computing KL×L² matrix M=(A^(T)B^(iT))

U^(T) where

defines the Kronecker product. Extracting M₁=M(:, 1) and M₂=M(:, 2:end); i.e, M₁ contains only the first column of M and M₂ is M, except for its first column. Computing (L²−1)×1 vector b=−M₂ ^(#)M₁, where (•)^(#) denotes pseudo-inverse. Computing L²×1 vector {tilde over (b)}=[1 b^(T)]^(T). Re-arranging {tilde over (b)} in rows of L to form updated B^(i) ⁺¹ .
 8. An apparatus for joint antenna array calibration and direction of arrival estimation comprising of: a radio frequency oscillator operable to generate a continuous wave or a pulsed output; an antenna array operable to transmit the output of the oscillator; an antenna array operable to receive the signal reflected from a plurality of objects; a processor operable to perform the steps of: setting i=0, setting B^(i) and Γ^(i) to initial values; estimating the data covariance matrix {circumflex over (R)}_(x); eigen decomposing {circumflex over (R)}_(x); finding U and K peaks in the 2D spectrum defined by P^(i)(θ, φ)=∥U^(H)B^(i)Γ^(i)a(θ, φ)∥⁻² where the peaks correspond to the direction of arrival (DOA) estimates; forming matrix A using the estimated DOA values; calculating matrix Z; estimating vector γ; forming diagonal matrix Qk; updating the estimated Γ; minimizing the cost function.
 9. The apparatus of claim 8, wherein: Said processor is further operable to estimate the covariance matrix by ${\hat{R}}_{x} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\; {{x(n)}{{x(n)}^{H}.}}}}$
 10. The apparatus of claim 8, wherein: Said processor is further operable to calculate matrix Z by $Z = {\sum\limits_{k = 1}^{K}\; {Q_{k}^{H}B^{i^{H}}{UU}^{H}B^{i}{Q_{k}.}}}$
 11. The apparatus of claim 8, wherein: Said processor is further operable to estimate vector γ where γ=Z⁻¹w/(w^(T)Z⁻¹w).
 12. The apparatus of claim 8, wherein: Said processor is further operable to form diagonal matrix QK using Qk=diag{a(θk, φk)}.
 13. The apparatus of claim 8, wherein: Said processor is further operable to update the estimated Γ using the diagonal elements of γ as Γ^(i) ⁺¹ =diag{γ}.
 14. The apparatus of claim 8, wherein: Said processor is further operable to perform the steps of: Calculating a cost factor by $J = {\sum\limits_{k = 1}^{K}\; {{U^{H}B\; \Gamma \; {a\left( {\theta_{k},\varphi_{k}} \right)}}}^{2}}$ Minimizing the cost factor by computing KL×L² matrix M=(A^(T)B^(iT))

U^(T), where

defines the Kronecker product. Extracting M₁=M(:, 1) and M₂=M(:, 2:end) ; i.e, M₁ contains only the first column of M and M₂ is M, except for its first column. Computing (L²−1)×1 vector b=−M₂ ^(#)M₁, where (•)¹⁹⁰ denotes pseudo-inverse. Computing L²×1 vector {tilde over (b)}=[1 b^(T)]^(T). Re-arranging {tilde over (b)} in rows of L to form updated B^(i) ⁺
 1. 